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最小均方误差的信噪比和信干噪比 lmssnr
一个用matlab来实现的最小均方误差的信噪比和信干噪比(Using matlab to achieve the minimum mean square error, signal to noise ratio and signal to interference noise ratio
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- 2020-07-10 01:08:57下载
- 积分：1
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语音信号降噪之小波分解法
语音信号处理--降噪方法之小波分解法 MATLAB例程(Speech Signal Processing--A MATLAB Routine of Wavelet Decomposition Method for Noise Reduction)
- 2020-07-04 19:00:01下载
- 积分：1
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dtw_revised
利用DTW模版匹配算法实现0~9十个数字的识别。(use DTW template matching algorithm 0-9 10-digit identification.)
- 2007-06-11 16:51:28下载
- 积分：1
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TTS-SDK
科大讯飞关于语音识别开发SDK库，支持语音转汉字功能及根据读汉字功能。有兴趣的可以好好研究研究(IFLYTEK speech recognition development SDK library are interested in a good studies)
- 2012-09-14 12:42:31下载
- 积分：1
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提取语音信号基频
用自相关函数提取语音信号基频，提取音频文件的基频等高线(Use the autocorrelation function on segments of the signal (windowsize: 100ms) and compute the fundamental frequency. Use a max_time_lag of 100ms in the autocorrelation function and a window shift of 25ms. Create a fundamental frequency vector and plot the pitch contour.)
- 2017-09-14 09:23:15下载
- 积分：1
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speaker
本系统是基于矢量量化技术的说话人识别系统。可根据语音库以及实时录音识别且具有图形用户界面。压缩包内含有源码、语音信息库音频及演示视频等。(The system is based on vector quantization speaker recognition system. Identifiable and has a graphical user interface based on speech and real-time recording. Within the compressed package contains the source code, voice information and presentation of audio and video.)
- 2015-04-16 15:19:25下载
- 积分：1
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yycl
提取语音信号的lpc参数并进行时间归整，需要将wav语音文件放在指定目录下‘e:yyzl’(voice signal from the lpc parameters and time consolidation, need to wav sound files on the specified directory 'e : yyzl')
- 2006-11-28 12:11:09下载
- 积分：1
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GenderRecognition
从一段音频中识别说话人的性别。开发环境vs2005(To identify the audio from a speaker' s gender. Development Environment vs2005)
- 2009-10-25 00:41:52下载
- 积分：1
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lpcc
用matlab实现LPCC特征的提取的程序！(Using matlab realize LPCC feature extraction procedure!)
- 2008-03-18 12:31:40下载
- 积分：1
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ABSE
熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)
- 2020-11-02 21:29:54下载
- 积分：1
